Overview

Dataset statistics

Number of variables16
Number of observations13640
Missing cells0
Missing cells (%)0.0%
Duplicate rows240
Duplicate rows (%)1.8%
Total size in memory1.7 MiB
Average record size in memory128.0 B

Variable types

Numeric9
Categorical6
Text1

Alerts

Property Type has constant value ""Constant
Dataset has 240 (1.8%) duplicate rowsDuplicates
Condo is highly overall correlated with Parking and 1 other fieldsHigh correlation
Negotiation Type is highly overall correlated with PriceHigh correlation
Parking is highly overall correlated with Condo and 4 other fieldsHigh correlation
Price is highly overall correlated with Negotiation TypeHigh correlation
Rooms is highly overall correlated with Parking and 1 other fieldsHigh correlation
Size is highly overall correlated with Condo and 4 other fieldsHigh correlation
Suites is highly overall correlated with Parking and 2 other fieldsHigh correlation
Toilets is highly overall correlated with Parking and 2 other fieldsHigh correlation
New is highly imbalanced (88.4%)Imbalance
Condo has 1977 (14.5%) zerosZeros
Suites has 3285 (24.1%) zerosZeros
Parking has 570 (4.2%) zerosZeros
Latitude has 881 (6.5%) zerosZeros
Longitude has 881 (6.5%) zerosZeros

Reproduction

Analysis started2025-02-01 22:02:34.941378
Analysis finished2025-02-01 22:03:10.742233
Duration35.8 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

Price
Real number (ℝ)

HIGH CORRELATION 

Distinct1881
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287737.78
Minimum480
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2025-02-01T19:03:11.570720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum480
5-th percentile1100
Q11858.75
median8100
Q3360000
95-th percentile1180000
Maximum10000000
Range9999520
Interquartile range (IQR)358141.25

Descriptive statistics

Standard deviation590821.42
Coefficient of variation (CV)2.0533328
Kurtosis55.498831
Mean287737.78
Median Absolute Deviation (MAD)7200
Skewness5.9355903
Sum3.9247434 × 109
Variance3.4906995 × 1011
MonotonicityNot monotonic
2025-02-01T19:03:12.484150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 367
 
2.7%
1200 362
 
2.7%
2500 299
 
2.2%
1300 276
 
2.0%
2000 276
 
2.0%
1600 271
 
2.0%
1400 243
 
1.8%
1100 238
 
1.7%
1800 237
 
1.7%
1000 232
 
1.7%
Other values (1871) 10839
79.5%
ValueCountFrequency (%)
480 1
 
< 0.1%
500 5
 
< 0.1%
550 1
 
< 0.1%
600 7
0.1%
610 1
 
< 0.1%
628 1
 
< 0.1%
630 2
 
< 0.1%
650 13
0.1%
660 3
 
< 0.1%
670 1
 
< 0.1%
ValueCountFrequency (%)
10000000 1
< 0.1%
9979947 1
< 0.1%
8500000 1
< 0.1%
8039200 1
< 0.1%
8000000 2
< 0.1%
7765616 1
< 0.1%
7559420 2
< 0.1%
7521000 1
< 0.1%
7500000 2
< 0.1%
7200000 1
< 0.1%

Condo
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1415
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean689.88233
Minimum0
Maximum9500
Zeros1977
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2025-02-01T19:03:13.243680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1290
median500
Q3835
95-th percentile2057.6
Maximum9500
Range9500
Interquartile range (IQR)545

Descriptive statistics

Standard deviation757.64936
Coefficient of variation (CV)1.0982298
Kurtosis16.932039
Mean689.88233
Median Absolute Deviation (MAD)257
Skewness3.1985952
Sum9409995
Variance574032.56
MonotonicityNot monotonic
2025-02-01T19:03:13.953241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1977
 
14.5%
400 303
 
2.2%
500 300
 
2.2%
350 269
 
2.0%
600 267
 
2.0%
450 258
 
1.9%
550 226
 
1.7%
700 217
 
1.6%
300 209
 
1.5%
650 171
 
1.3%
Other values (1405) 9443
69.2%
ValueCountFrequency (%)
0 1977
14.5%
1 27
 
0.2%
3 1
 
< 0.1%
6 1
 
< 0.1%
10 4
 
< 0.1%
15 1
 
< 0.1%
20 2
 
< 0.1%
25 2
 
< 0.1%
30 1
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
9500 1
< 0.1%
8920 1
< 0.1%
8860 1
< 0.1%
8800 1
< 0.1%
8000 1
< 0.1%
7928 1
< 0.1%
7800 1
< 0.1%
7500 2
< 0.1%
7428 1
< 0.1%
7200 1
< 0.1%

Size
Real number (ℝ)

HIGH CORRELATION 

Distinct339
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.3739
Minimum30
Maximum880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2025-02-01T19:03:14.649809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile39
Q150
median65
Q394
95-th percentile201
Maximum880
Range850
Interquartile range (IQR)44

Descriptive statistics

Standard deviation58.435676
Coefficient of variation (CV)0.69258001
Kurtosis15.698323
Mean84.3739
Median Absolute Deviation (MAD)17
Skewness3.0813914
Sum1150860
Variance3414.7283
MonotonicityNot monotonic
2025-02-01T19:03:15.273422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 728
 
5.3%
60 514
 
3.8%
70 484
 
3.5%
48 412
 
3.0%
65 395
 
2.9%
45 353
 
2.6%
55 349
 
2.6%
56 303
 
2.2%
40 270
 
2.0%
47 257
 
1.9%
Other values (329) 9575
70.2%
ValueCountFrequency (%)
30 73
0.5%
31 34
 
0.2%
32 48
 
0.4%
33 42
 
0.3%
34 56
 
0.4%
35 157
1.2%
36 60
 
0.4%
37 70
0.5%
38 99
0.7%
39 59
 
0.4%
ValueCountFrequency (%)
880 1
 
< 0.1%
852 1
 
< 0.1%
670 1
 
< 0.1%
640 1
 
< 0.1%
627 1
 
< 0.1%
620 1
 
< 0.1%
600 1
 
< 0.1%
598 1
 
< 0.1%
574 1
 
< 0.1%
540 3
< 0.1%

Rooms
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3120235
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2025-02-01T19:03:15.797097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.77746074
Coefficient of variation (CV)0.33626853
Kurtosis0.66764682
Mean2.3120235
Median Absolute Deviation (MAD)1
Skewness0.34651822
Sum31536
Variance0.60444521
MonotonicityNot monotonic
2025-02-01T19:03:16.282795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 6766
49.6%
3 4307
31.6%
1 1740
 
12.8%
4 802
 
5.9%
5 20
 
0.1%
6 3
 
< 0.1%
10 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
1 1740
 
12.8%
2 6766
49.6%
3 4307
31.6%
4 802
 
5.9%
5 20
 
0.1%
6 3
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
7 1
 
< 0.1%
6 3
 
< 0.1%
5 20
 
0.1%
4 802
 
5.9%
3 4307
31.6%
2 6766
49.6%
1 1740
 
12.8%

Toilets
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0736804
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2025-02-01T19:03:16.722523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9618026
Coefficient of variation (CV)0.4638143
Kurtosis3.3228095
Mean2.0736804
Median Absolute Deviation (MAD)0
Skewness1.5961676
Sum28285
Variance0.92506425
MonotonicityNot monotonic
2025-02-01T19:03:17.279176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 8190
60.0%
1 3187
 
23.4%
3 940
 
6.9%
4 818
 
6.0%
5 422
 
3.1%
6 67
 
0.5%
7 14
 
0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
1 3187
 
23.4%
2 8190
60.0%
3 940
 
6.9%
4 818
 
6.0%
5 422
 
3.1%
6 67
 
0.5%
7 14
 
0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 14
 
0.1%
6 67
 
0.5%
5 422
 
3.1%
4 818
 
6.0%
3 940
 
6.9%
2 8190
60.0%
1 3187
 
23.4%

Suites
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98079179
Minimum0
Maximum6
Zeros3285
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2025-02-01T19:03:17.812846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83489092
Coefficient of variation (CV)0.85124175
Kurtosis2.9845746
Mean0.98079179
Median Absolute Deviation (MAD)0
Skewness1.4535991
Sum13378
Variance0.69704285
MonotonicityNot monotonic
2025-02-01T19:03:18.327527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 8669
63.6%
0 3285
 
24.1%
3 827
 
6.1%
2 607
 
4.5%
4 247
 
1.8%
5 4
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 3285
 
24.1%
1 8669
63.6%
2 607
 
4.5%
3 827
 
6.1%
4 247
 
1.8%
5 4
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 4
 
< 0.1%
4 247
 
1.8%
3 827
 
6.1%
2 607
 
4.5%
1 8669
63.6%
0 3285
 
24.1%

Parking
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3931818
Minimum0
Maximum9
Zeros570
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size106.7 KiB
2025-02-01T19:03:18.877188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.82993193
Coefficient of variation (CV)0.5957097
Kurtosis5.2611272
Mean1.3931818
Median Absolute Deviation (MAD)0
Skewness1.8393646
Sum19003
Variance0.688787
MonotonicityNot monotonic
2025-02-01T19:03:19.347895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 8899
65.2%
2 2974
 
21.8%
3 757
 
5.5%
0 570
 
4.2%
4 347
 
2.5%
5 72
 
0.5%
6 14
 
0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 570
 
4.2%
1 8899
65.2%
2 2974
 
21.8%
3 757
 
5.5%
4 347
 
2.5%
5 72
 
0.5%
6 14
 
0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 2
 
< 0.1%
7 4
 
< 0.1%
6 14
 
0.1%
5 72
 
0.5%
4 347
 
2.5%
3 757
 
5.5%
2 2974
 
21.8%
1 8899
65.2%
0 570
 
4.2%

Elevator
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size106.7 KiB
0
8809 
1
4831 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13640
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 8809
64.6%
1 4831
35.4%

Length

2025-02-01T19:03:19.818602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-01T19:03:20.123414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 8809
64.6%
1 4831
35.4%

Most occurring characters

ValueCountFrequency (%)
0 8809
64.6%
1 4831
35.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13640
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8809
64.6%
1 4831
35.4%

Most occurring scripts

ValueCountFrequency (%)
Common 13640
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8809
64.6%
1 4831
35.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8809
64.6%
1 4831
35.4%

Furnished
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size106.7 KiB
0
11638 
1
2002 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13640
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11638
85.3%
1 2002
 
14.7%

Length

2025-02-01T19:03:20.427223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-01T19:03:20.714046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 11638
85.3%
1 2002
 
14.7%

Most occurring characters

ValueCountFrequency (%)
0 11638
85.3%
1 2002
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13640
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11638
85.3%
1 2002
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common 13640
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11638
85.3%
1 2002
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11638
85.3%
1 2002
 
14.7%

Swimming Pool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size106.7 KiB
1
6986 
0
6654 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13640
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 6986
51.2%
0 6654
48.8%

Length

2025-02-01T19:03:21.010865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-01T19:03:21.287690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6986
51.2%
0 6654
48.8%

Most occurring characters

ValueCountFrequency (%)
1 6986
51.2%
0 6654
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13640
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6986
51.2%
0 6654
48.8%

Most occurring scripts

ValueCountFrequency (%)
Common 13640
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6986
51.2%
0 6654
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6986
51.2%
0 6654
48.8%

New
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size106.7 KiB
0
13427 
1
 
213

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13640
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13427
98.4%
1 213
 
1.6%

Length

2025-02-01T19:03:21.595499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-01T19:03:21.876326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13427
98.4%
1 213
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 13427
98.4%
1 213
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13640
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13427
98.4%
1 213
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 13640
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13427
98.4%
1 213
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13427
98.4%
1 213
 
1.6%
Distinct96
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size106.7 KiB
2025-02-01T19:03:22.437980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length23
Mean length19.46239
Min length12

Characters and Unicode

Total characters265467
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArtur Alvim/São Paulo
2nd rowArtur Alvim/São Paulo
3rd rowArtur Alvim/São Paulo
4th rowArtur Alvim/São Paulo
5th rowArtur Alvim/São Paulo
ValueCountFrequency (%)
paulo 13640
39.8%
vila 1814
 
5.3%
campo 513
 
1.5%
são 487
 
1.4%
pinheiros/são 479
 
1.4%
cidade 437
 
1.3%
jardim 398
 
1.2%
moema/são 293
 
0.9%
mooca/são 288
 
0.8%
itaim 286
 
0.8%
Other values (109) 15601
45.6%
2025-02-01T19:03:23.376395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 35673
13.4%
a 32904
12.4%
20596
 
7.8%
l 18693
 
7.0%
u 18582
 
7.0%
P 15612
 
5.9%
S 15401
 
5.8%
ã 15229
 
5.7%
/ 13640
 
5.1%
i 10691
 
4.0%
Other values (44) 68446
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 183805
69.2%
Uppercase Letter 47426
 
17.9%
Space Separator 20596
 
7.8%
Other Punctuation 13640
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 35673
19.4%
a 32904
17.9%
l 18693
10.2%
u 18582
10.1%
ã 15229
8.3%
i 10691
 
5.8%
e 8786
 
4.8%
r 8252
 
4.5%
n 5438
 
3.0%
d 5261
 
2.9%
Other values (21) 24296
13.2%
Uppercase Letter
ValueCountFrequency (%)
P 15612
32.9%
S 15401
32.5%
C 2664
 
5.6%
V 2312
 
4.9%
B 2132
 
4.5%
M 1940
 
4.1%
A 1209
 
2.5%
L 1190
 
2.5%
J 1139
 
2.4%
R 898
 
1.9%
Other values (11) 2929
 
6.2%
Space Separator
ValueCountFrequency (%)
20596
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 13640
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 231231
87.1%
Common 34236
 
12.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 35673
15.4%
a 32904
14.2%
l 18693
 
8.1%
u 18582
 
8.0%
P 15612
 
6.8%
S 15401
 
6.7%
ã 15229
 
6.6%
i 10691
 
4.6%
e 8786
 
3.8%
r 8252
 
3.6%
Other values (42) 51408
22.2%
Common
ValueCountFrequency (%)
20596
60.2%
/ 13640
39.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246911
93.0%
None 18556
 
7.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 35673
14.4%
a 32904
13.3%
20596
 
8.3%
l 18693
 
7.6%
u 18582
 
7.5%
P 15612
 
6.3%
S 15401
 
6.2%
/ 13640
 
5.5%
i 10691
 
4.3%
e 8786
 
3.6%
Other values (33) 56333
22.8%
None
ValueCountFrequency (%)
ã 15229
82.1%
é 802
 
4.3%
í 542
 
2.9%
á 538
 
2.9%
ç 494
 
2.7%
ú 388
 
2.1%
Ó 137
 
0.7%
ô 134
 
0.7%
Á 133
 
0.7%
â 84
 
0.5%

Negotiation Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size106.7 KiB
rent
7228 
sale
6412 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters54560
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrent
2nd rowrent
3rd rowrent
4th rowrent
5th rowrent

Common Values

ValueCountFrequency (%)
rent 7228
53.0%
sale 6412
47.0%

Length

2025-02-01T19:03:23.747165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-01T19:03:24.028990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
rent 7228
53.0%
sale 6412
47.0%

Most occurring characters

ValueCountFrequency (%)
e 13640
25.0%
r 7228
13.2%
n 7228
13.2%
t 7228
13.2%
s 6412
11.8%
a 6412
11.8%
l 6412
11.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54560
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13640
25.0%
r 7228
13.2%
n 7228
13.2%
t 7228
13.2%
s 6412
11.8%
a 6412
11.8%
l 6412
11.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 54560
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13640
25.0%
r 7228
13.2%
n 7228
13.2%
t 7228
13.2%
s 6412
11.8%
a 6412
11.8%
l 6412
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13640
25.0%
r 7228
13.2%
n 7228
13.2%
t 7228
13.2%
s 6412
11.8%
a 6412
11.8%
l 6412
11.8%

Property Type
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size106.7 KiB
apartment
13640 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters122760
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowapartment
2nd rowapartment
3rd rowapartment
4th rowapartment
5th rowapartment

Common Values

ValueCountFrequency (%)
apartment 13640
100.0%

Length

2025-02-01T19:03:24.334800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-01T19:03:24.605634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
apartment 13640
100.0%

Most occurring characters

ValueCountFrequency (%)
a 27280
22.2%
t 27280
22.2%
p 13640
11.1%
r 13640
11.1%
m 13640
11.1%
e 13640
11.1%
n 13640
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 122760
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 27280
22.2%
t 27280
22.2%
p 13640
11.1%
r 13640
11.1%
m 13640
11.1%
e 13640
11.1%
n 13640
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 122760
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 27280
22.2%
t 27280
22.2%
p 13640
11.1%
r 13640
11.1%
m 13640
11.1%
e 13640
11.1%
n 13640
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 27280
22.2%
t 27280
22.2%
p 13640
11.1%
r 13640
11.1%
m 13640
11.1%
e 13640
11.1%
n 13640
11.1%

Latitude
Real number (ℝ)

ZEROS 

Distinct8405
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.077047
Minimum-46.749039
Maximum0
Zeros881
Zeros (%)6.5%
Negative12759
Negative (%)93.5%
Memory size106.7 KiB
2025-02-01T19:03:24.926433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-46.749039
5-th percentile-23.661191
Q1-23.594552
median-23.552813
Q3-23.51764
95-th percentile0
Maximum0
Range46.749039
Interquartile range (IQR)0.0769119

Descriptive statistics

Standard deviation5.8666334
Coefficient of variation (CV)-0.26573452
Kurtosis10.361152
Mean-22.077047
Median Absolute Deviation (MAD)0.039699
Skewness3.3370194
Sum-301130.92
Variance34.417387
MonotonicityNot monotonic
2025-02-01T19:03:25.330184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 881
 
6.5%
-23.5053909 57
 
0.4%
-23.604294 43
 
0.3%
-26.9225117 32
 
0.2%
-23.5002428 25
 
0.2%
-23.714221 25
 
0.2%
-23.5453953 22
 
0.2%
-23.522756 21
 
0.2%
-23.58575 20
 
0.1%
-23.585748 18
 
0.1%
Other values (8395) 12496
91.6%
ValueCountFrequency (%)
-46.749039 1
< 0.1%
-46.734483 1
< 0.1%
-46.715115 1
< 0.1%
-46.700223 1
< 0.1%
-46.678478 1
< 0.1%
-46.677847 1
< 0.1%
-46.669247 1
< 0.1%
-46.668966 1
< 0.1%
-46.656944 1
< 0.1%
-46.655399 1
< 0.1%
ValueCountFrequency (%)
0 881
6.5%
-1.3641735 1
 
< 0.1%
-14.2112427 1
 
< 0.1%
-15.5314939 1
 
< 0.1%
-21.8577415 1
 
< 0.1%
-22.3292281 1
 
< 0.1%
-22.4605143 1
 
< 0.1%
-22.7862985 1
 
< 0.1%
-22.8329885 2
 
< 0.1%
-23.174031 1
 
< 0.1%

Longitude
Real number (ℝ)

ZEROS 

Distinct8451
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-43.597088
Minimum-58.364352
Maximum0
Zeros881
Zeros (%)6.5%
Negative12759
Negative (%)93.5%
Memory size106.7 KiB
2025-02-01T19:03:25.713946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-58.364352
5-th percentile-46.744902
Q1-46.681671
median-46.637255
Q3-46.56004
95-th percentile0
Maximum0
Range58.364352
Interquartile range (IQR)0.12163125

Descriptive statistics

Standard deviation11.487288
Coefficient of variation (CV)-0.26348751
Kurtosis10.423235
Mean-43.597088
Median Absolute Deviation (MAD)0.05315615
Skewness3.5205647
Sum-594664.28
Variance131.95779
MonotonicityNot monotonic
2025-02-01T19:03:26.117696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 881
 
6.5%
-46.6227832 57
 
0.4%
-46.518325 43
 
0.3%
-49.0607072 32
 
0.2%
-46.533612 26
 
0.2%
-46.6276118 25
 
0.2%
-46.704675 25
 
0.2%
-46.6167926 22
 
0.2%
-46.655492 21
 
0.2%
-46.533696 18
 
0.1%
Other values (8441) 12490
91.6%
ValueCountFrequency (%)
-58.3643522 1
 
< 0.1%
-51.9763575 1
 
< 0.1%
-49.3378145 1
 
< 0.1%
-49.108049 1
 
< 0.1%
-49.0607072 32
0.2%
-49.0606445 14
0.1%
-48.2698339 1
 
< 0.1%
-47.3454718 1
 
< 0.1%
-47.1658588 2
 
< 0.1%
-47.008401 1
 
< 0.1%
ValueCountFrequency (%)
0 881
6.5%
-23.468007 1
 
< 0.1%
-23.507452 1
 
< 0.1%
-23.51764 1
 
< 0.1%
-23.518222 1
 
< 0.1%
-23.518756 1
 
< 0.1%
-23.534683 1
 
< 0.1%
-23.535175 1
 
< 0.1%
-23.540783 1
 
< 0.1%
-23.545329 1
 
< 0.1%

Interactions

2025-02-01T19:03:04.702919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:38.783993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:41.938037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:44.755292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:47.686474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:51.017408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:54.107490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:57.737240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:01.227074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:05.185619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:39.110794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:42.224861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:45.045110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:48.116208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:51.312227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:54.676138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:58.127997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:01.625828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:05.586372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:39.547524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:42.478703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:45.305950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:48.562931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:51.580059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:55.049907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:58.484775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:01.983605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:05.967134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:39.853331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:42.933423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:45.563789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:49.009654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:51.862884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:55.405686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:58.848549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:02.338386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:06.638717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:40.130160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:43.324179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:45.974535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:49.297473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:52.283621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:55.771459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:59.221318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:02.704158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:07.111426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:40.445965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:43.623993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:46.406268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:49.586296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:52.676377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:56.189199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:59.635064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:03.183861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:07.502182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:40.764766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:43.929802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:46.792028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:50.019027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:52.988185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:56.578959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:00.111769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:03.556632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:07.885944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:41.033599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:44.187645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:47.089844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:50.273870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:53.277006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:56.962720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:00.449558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:03.920405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:08.265709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:41.409366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:44.466470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:47.371670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:50.735584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:53.643779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:02:57.336487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:00.832320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-01T19:03:04.293175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-02-01T19:03:26.723319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CondoElevatorFurnishedLatitudeLongitudeNegotiation TypeNewParkingPriceRoomsSizeSuitesSwimming PoolToilets
Condo1.0000.0260.093-0.155-0.3180.1470.0510.5070.0930.4020.6140.3470.1230.388
Elevator0.0261.0000.066-0.058-0.1010.1200.1690.0700.1500.0350.0270.1900.2040.215
Furnished0.0930.0661.000-0.043-0.0710.0780.0440.0910.038-0.0500.0310.0830.1470.084
Latitude-0.155-0.058-0.0431.0000.3790.0500.000-0.133-0.036-0.083-0.122-0.1900.036-0.184
Longitude-0.318-0.101-0.0710.3791.0000.1080.000-0.224-0.073-0.134-0.266-0.2310.104-0.246
Negotiation Type0.1470.1200.0780.0500.1081.0000.126-0.0650.8650.010-0.099-0.0410.051-0.024
New0.0510.1690.0440.0000.0000.1261.000-0.0190.088-0.015-0.067-0.0480.000-0.031
Parking0.5070.0700.091-0.133-0.224-0.065-0.0191.0000.2540.5700.6510.5850.3260.601
Price0.0930.1500.038-0.036-0.0730.8650.0880.2541.0000.2400.2760.2060.0970.239
Rooms0.4020.035-0.050-0.083-0.1340.010-0.0150.5700.2401.0000.7490.4590.2010.498
Size0.6140.0270.031-0.122-0.266-0.099-0.0670.6510.2760.7491.0000.5080.1220.558
Suites0.3470.1900.083-0.190-0.231-0.041-0.0480.5850.2060.4590.5081.0000.2810.928
Swimming Pool0.1230.2040.1470.0360.1040.0510.0000.3260.0970.2010.1220.2811.0000.274
Toilets0.3880.2150.084-0.184-0.246-0.024-0.0310.6010.2390.4980.5580.9280.2741.000

Missing values

2025-02-01T19:03:08.867336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-01T19:03:09.809752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PriceCondoSizeRoomsToiletsSuitesParkingElevatorFurnishedSwimming PoolNewDistrictNegotiation TypeProperty TypeLatitudeLongitude
09302204722110000Artur Alvim/São Paulorentapartment-23.543138-46.479486
110001484522110000Artur Alvim/São Paulorentapartment-23.550239-46.480718
210001004822110000Artur Alvim/São Paulorentapartment-23.542818-46.485665
310002004822110000Artur Alvim/São Paulorentapartment-23.547171-46.483014
413004105522111000Artur Alvim/São Paulorentapartment-23.525025-46.482436
5117005022110000Artur Alvim/São Paulorentapartment-23.548751-46.477195
610001805212111000Artur Alvim/São Paulorentapartment-23.549840-46.484137
79001504022110000Artur Alvim/São Paulorentapartment-23.539740-46.492670
8100006522110000Artur Alvim/São Paulorentapartment-23.548751-46.477195
91000010022110000Artur Alvim/São Paulorentapartment-23.548751-46.477195
PriceCondoSizeRoomsToiletsSuitesParkingElevatorFurnishedSwimming PoolNewDistrictNegotiation TypeProperty TypeLatitudeLongitude
136302800004805521010000Jabaquara/São Paulosaleapartment-23.652529-46.647468
136313500005546821010000Jabaquara/São Paulosaleapartment-23.659879-46.639641
136328000009968632120010Jabaquara/São Paulosaleapartment-23.647569-46.641989
136333000003704721010000Jabaquara/São Paulosaleapartment-23.640301-46.652619
136344560007708531010000Jabaquara/São Paulosaleapartment-23.773509-46.675631
136352650004205121010000Jabaquara/São Paulosaleapartment-23.653004-46.635463
136365450006307432120010Jabaquara/São Paulosaleapartment-23.648930-46.641982
13637515000110011433110010Jabaquara/São Paulosaleapartment-23.649693-46.649783
13638345000483912110110Jabaquara/São Paulosaleapartment-23.652060-46.637046
1363916198704421010000Jardim Ângela/São Paulosaleapartment-23.613391-46.523109

Duplicate rows

Most frequently occurring

PriceCondoSizeRoomsToiletsSuitesParkingElevatorFurnishedSwimming PoolNewDistrictNegotiation TypeProperty TypeLatitudeLongitude# duplicates
4212955006932110000Jardim Ângela/São Paulorentapartment-23.604294-46.51832510
1488000140013232110000Iguatemi/São Paulorentapartment-23.585672-46.6812168
72160005021011000Rio Pequeno/São Paulorentapartment-23.565075-46.7506467
2911905557032110010Jardim Ângela/São Paulorentapartment-23.604294-46.5183256
5814003305622110000Sapopemba/São Paulorentapartment-23.585748-46.5336966
2211002704411010000Jabaquara/São Paulorentapartment-23.655714-46.6363305
6515002733111010010Brás/São Paulorentapartment-23.545395-46.6167935
10825004564911010010Casa Verde/São Paulorentapartment0.0000000.0000005
147720019006212120100Iguatemi/São Paulorentapartment-23.583830-46.6835415
18019900003722110000Perus/São Paulosaleapartment-23.406884-46.7370375